Neural correlates of risk prediction error during reinforcement learning in humans

Behavioral studies have shown for decades that humans are sensitive to risk when making decisions. More recently, brain activities have been shown to be correlated with risky choices. But an important gap needs to be filled: How does the human brain learn which decisions are risky? In cognitive neuroscience, reinforcement learning has never been used to estimate reward variance, a common measure of risk in economics and psychology. It is thus unknown which brain regions are involved in risk learning. To address this question, participants completed a decision-making task during fMRI. They chose repetitively from four decks of cards and each selection was followed by a stochastic payoff. Expected reward and risk differed among the decks. Participants' aim was to maximize payoffs. Risk and reward prediction errors were calculated after each payoff based on a novel reinforcement learning model. For reward prediction error, the strongest correlation was found with the BOLD response in the striatum. For risk prediction error, the strongest correlation was found with the BOLD responses in the insula and inferior frontal gyrus. We conclude that risk and reward prediction errors are processed by distinct neural circuits during reinforcement learning. Additional analyses revealed that the BOLD response in the inferior frontal gyrus was more pronounced for risk aversive participants, suggesting that this region also serves to inhibit risky choices.